class_docstr: Experimental helper to perform optimization using torch
  workflow with a nevergrad optimizer
  
  Parameters
  ----------
  parameters: iterable
      module parameters which need to be optimized
  cls: Optimizer-like object
      name of a nevergrad optimizer, or nevergrad optimizer class, or ConfiguredOptimizer instance
  bound: float
      values are clipped to [-bound, bound]
  
  Notes
  -----
  - This is experimental, the API may evolve
  - This does not support parallelization (multiple asks).
  
  
  Example
  -------
  ..code::python
  
      module = ...
      optimizer = helpers.TorchOptimizer(module.parameters(), "OnePlusOne")
      for x, y in batcher():
          loss = compute_loss(module(x), y)
          optimizer.step(loss)
code: def _set_candidate(self) -> None:
      for p, data in zip(self.parameters, self.candidate.value):
          # pylint:disable=not-callable
          p.data = torch.tensor(data.astype(np.float32))
  
file_path: nevergrad/nevergrad/optimization/helpers.py
imports: import inspect
  import typing
  import numpy
  import nevergrad
  from nevergrad.common.tools import pytorch_import_fix
  from .None import base
  from .None import optimizerlib
label_desc: Use image node_id 2 for calling the TorchOptimizer obj's underlying member method code with example usage: obj._set_candidate() without return types